用冻结音频编码器比较四种发声语料的声学事件积累与序列依赖结构
Structure Across Voices: Comparing acoustic-event type accumulation and sequence dependence across four vocal corpora using frozen audio encoders
AI 导读
一项 arXiv 研究提出匹配比较方法，用冻结音频编码器提供统一声学表示，对抹香鲸 codas、人类语音音素、斑胸草雀音节和普通狨呼叫四种语料进行声学与序列分析。结果显示抹香鲸录音中新声学形式持续出现且序列组织与重复性强，雀鸟录音在时间组织上最强。该方法无需将各语料强行纳入同一事件定义即可度量声学多样性与时间组织。
正文 · 原文
Abstract:Comparing acoustic structure across very different vocal corpora is difficult because the recordings use different native event units, contain different amounts of sequence, and were collected under different conditions. We develop a matched procedure for comparing such corpora without forcing them into the same event definition. Frozen audio encoders provide a common acoustic representation for recorded events, while matched sampling gives each corpus the same amount of evidence for acoustic and sequence analysis. We apply this procedure to sperm-whale codas, human speech phones, Bengalese-finch syllables, and common-marmoset calls. The comparison reveals different patterns across the four corpora. Sperm-whale recordings stand out broadly: new acoustic forms continue to appear, while their sequences also show strong organization and repetition. Finch recordings show the strongest organization of sounds over time, especially in how neighboring sounds and repeated patterns are arranged. Direct temporal and spectral measurements show that these differences are also present in the recorded sounds themselves. The comparison provides a practical way to measure acoustic diversity and temporal organization across very different vocal corpora by combining a common acoustic representation with matched observation opportunity, without treating their native sounds as equivalent.
| Comments: | 12 pages, 4 figures, 2 tables |
| Subjects: | Sound (cs.SD); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.16612 [cs.SD] |
| (or arXiv:2609.16612v2 [cs.SD] for this version) | |
| https://doi.org/10.48550/arXiv.2609.16612 arXiv-issued DOI via DataCite |
Submission history
From: Mudit Sinha [view email]
[v1]
Tue, 15 Sep 2026 04:19:03 UTC (159 KB)
[v2]
Sat, 24 Oct 2026 04:10:16 UTC (936 KB)
来源：arXiv cs.AI · arxiv.org